AI 中文总结
本研究在真实信用卡交易数据上对量子核k-means与经典聚类做受控基准测试，未发现稳健量子优势，同时提出低成本的搜索预算消融方法可有效鉴别程序伪影。
AI 中文摘要
我们在真实交易数据上开展了量子核$k$-means与经典聚类的信用卡欺诈检测基准测试，量子比特数最高达\MaxQubits{}，测试协议采用分离的选择与报告数据集且匹配搜索预算。我们未发现稳健的量子优势：性能差异的正负取决于寄存器大小，所有效应量均低于$0.013$ ARI，且我们观测到的唯一显著优势完全可由搜索的配置数量解释。我们进一步表明，普通超参数选择带来的性能变化远大于量子核，额外量子比特会通过核集中效应降低而非提升性能，且聚类框架本身在真实类别不平衡场景下失效，但基于核的异常评分不会。我们认为方法学贡献更具持久价值，尤其是搜索预算消融实验成本低且在本研究中起决定性作用：它将统计显著的优势转化为程序伪影，我们建议将其作为常规操作使用。
英文摘要
We benchmarked quantum kernel $k$-means against classical clustering for credit-card fraud detection on real transaction data, at up to \MaxQubits{} qubits, under a protocol with separated selection and reporting data and matched search budgets. We find no robust quantum advantage: the sign of the difference depends on register size, all effect sizes are below $0.013$ ARI, and the single significant advantage we observe is fully explained by the number of configurations searched. We further show that ordinary hyperparameter choices move performance by considerably more than the quantum kernel does, that additional qubits degrade rather than improve performance through kernel concentration, and that the clustering framing itself fails at realistic class imbalance though kernel-based anomaly scoring does not. We regard the methodological contribution as the more durable one. The search-budget ablation in particular is inexpensive and, in our case, decisive: it converted a statistically significant advantage into a procedural artefact. We would encourage its routine use.
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